Hyperpectral Signatures and Spectral Resolution Using ENVI - Tutorial of ENVI Software - Completely GIS, GPS, and Remote Sensing Lecture Material - facegis.com
Hyperpectral Signatures and Spectral Resolution Using ENVI

Overview of This Tutorial

This tutorial compares spectral resolution for several different sensors and the effect of resolution on the ability to discriminate and identify materials with distinct spectral signatures. The tutorial uses TM, GEOSCAN, GER63, and AVIRIS data from Cuprite, Nevada, USA, for intercomparison and comparison to materials from the USGS Spectral library.

Files Used in This Tutorial

You must have the ENVI TUTORIALS & DATA CD-ROM mounted on your system to access the files used by this tutorial, or copy the files to your disk.

Most of the files used in this tutorial are contained in the CUP_COMP subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM. The AVIRIS reflectance image file CUP95EFF.INT, its associated ENVI header file, and the extracted AVIRIS spectra are located in the C95AVSUB subdirectory.

Required Files

The files listed below, along with their associated .hdr files, are required to run this exercise. Optional spectral library files listed below may also be used if more detailed comparisons are desired. Selected data files have been converted to integer format by multiplying the reflectance values by 1000 because of disk space considerations. Values of 1000 in the files represent reflectance values of 1.0.

Required Files in the CUP_COMP Directory

USGS_EM.SLI	Subset of USGS Spectral Library
USGS_EM.HDR	ENVI Header for Above
CUPTM_RF.IMG	Cuprite TM reflectance subset
CUPTM_RF.HDR	ENVI Header for Above
CUPTM_EM.TXT	Kaolinite and Alunite average spectra from above
CUPGS_SB.IMT	Cuprite Geoscan Reflectance Image Subset
CUPGS_SB.HDR	ENVI Header for above
CUPGS_TM.TXT	Kaolinite and Alunite average spectra from above
CUPGERSB.IMG	Cuprite GER64 Reflectance Image Subset	
CUPGERSB.HDR	ENVI Header for above
CUPGEREM.TXT	Kaolinite and Alunite average spectra from above

Required Files in the C95AVSUB Directory

CUP95EFF.INT	Cuprite 1995 AVIRIS Reflectance Image Subset (in the C95AVSUB directory)
CUP95EFF.HDR	ENVI Header from above (in the C95AVSUB directory)
CUP95EFF.TXT	Kaolinite and Alunite average spectra from above in the C95AVSUB directory.

Optional Files (in the Default Spectral Library Directory)

USGS_MIN.SLI	USGS Spectral Library
USGS_MIN.HDR	ENVI Header for Above

Background

Spectral resolution determines the way we see individual spectral features in materials measured using imaging spectrometry. Many people confuse the terms spectral resolution with spectral sampling. These are very different. Spectral resolution refers to the width of an instrument response (band-pass) at half of the band depth (the Full Width Half Max [FWHM]). Spectral sampling usually refers to the band spacing - the quantization of the spectrum at discrete steps - and may be very different from the spectral resolution. Quality spectrometers are usually designed so that the band spacing is about equal to the band FWHM, which is why band spacing is often thought of as equal to spectral resolution. These are two different things, however, so be careful in your use of terms.

This exercise compares the effect of the spectral resolution of different sensors on the spectral signatures of minerals.


Spectral Modeling and Resolution

Spectral modeling shows that spectral resolution requirements for imaging spectrometers depend upon the character of the material being measured. For example, for the mineral kaolinite, shown in the plot below, we are still able to distinguish the characteristic doublet near 2.2 mm at 20 nm resolution. Even at 40 nm resolution, the asymmetrical shape of the band may be enough to identify the mineral, even though the spectral features have not been fully resolved.Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.com

Figure 1: Modeled effect of spectral resolution on the appearance of spectral features of kaolinite. Spectral resolution from top to bottom: 5, 10, 20, 40, and 80 nm resolution.

The spectral resolution required for a specific sensor is a direct function of the material you are trying to identify, and the contrast between that material and the background materials. The following figure shows modeled spectra for the mineral kaolinite for several different sensors.

Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.com

Figure 2: Modeled signatures of different hyperspectral sensors for the mineral kaolinite. From Swayze, 1997.


Case History: Cuprite, Nevada, USA

This example is provided to illustrate the effects of spatial and spectral resolution on information extraction from multispectral/hyperspectral data. Several images of the Cuprite, Nevada, USA, area acquired with a variety of spectral and spatial resolutions serve as the basis for discussions on the effect of these parameters on mineralogic mapping using remote sensing techniques. These images have not been georeferenced, but image subsets covering approximately the same spatial areas are shown. Cuprite has been used extensively as a test site for remote sensing instrument validation (Abrams et al., 1978; Kahle and Goetz, 1983; Kruse et al., 1990; Hook et al., 1991). A generalized alteration map is provided for comparison with the images. Examples from Landsat TM, GEOSCAN MkII, GER63, and AVIRIS illustrate both spatial and spectral aspects. Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.com

Figure 3: Alteration map for Cuprite, Nevada.

All of these data sets have been calibrated to reflectance. Only three of the numerous materials present at the Cuprite site were used for the purposes of this comparison. Average kaolinite, alunite, and buddingtonite image spectra were selected from known occurrences at Cuprite. Laboratory spectra from the USGS Spectral Library (Clark et al., 1990) of the three selected minerals are provided for comparison to the image spectra. The following is a synopsis of selected instrument characteristics and a discussion of the images and spectra obtained with each sensor

Start ENVI

Before attempting to start the program, ensure that ENVI is properly installed as described in the installation guide.

  • To open ENVI in Unix, enter " envi " at the UNIX command line.
  • To open ENVI from a Windows or Macintosh system, d ouble-click on the ENVI icon.

The ENVI Main Menu appears when the program has successfully loaded and executed.

Open A Spectral Library File

To open a spectral library:

  1. Select Spectral Tools->Spectral Libraries-Spectral Library Viewer. Click on Open Spectral Library button to start the file selection dialog.

Note that on some platforms you must hold the left mouse button down to display the submenus from the Main Menu.

A file selection dialog appears.

  1. Navigate to the CUP_COMP subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM just as you would in any other application and select the file USGS_EM.SLI from the list and click "Open".

The Spectral Library Viewer dialog appears with four laboratory spectra for the Cuprite site listed.

View Library Spectra

This step uses library Spectra (approximately 10 nm Spectral Resolution) from the USGS Spectral Library.

  1. Plot each of the four spectra from the spectral library in an ENVI spectral plot by clicking on the spectrum name in the Spectral Library Viewer window.
  2. Examine the detail available in the library spectral plots paying special attention to the absorption feature positions, depths, and shapes near 2.2 - 2.4 mm. You may want to select Edit->Plot Parameters and change the X-Axis range to 2.0 - 2.5 mm to accomplish this comparison.
  3. Place this plot window to one side of the screen for use with data from the hyperspectral sensors.

Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.com

Figure 4: Laboratory measurements for the minerals kaolinite, alunite, and buddingtonite measured on the USGS Denver Beckman Spectrometer.

View Landsat TM Image and Spectra

This dataset is Landsat Thematic Mapper data with spatial resolution of 30 meters and spectral resolution of up to 100nm. The Cuprite TM data were acquired on 4 October 1984 and are in the public domain. The figure below is a plot of the Region of Interest (ROI) average spectra for the three materials shown in the library spectra above. The small squares indicate the TM band 7 (2.21 mm) center point. The lines indicate the slope from TM band 5 (1.65 mm). Note the similarity of all of the "spectra" and how it is not possible to discriminate between the three endmembers.

Display Reflectance Image

  1. Select File->Open Image File from the ENVI main menu and open the file CUPTM_RF.IMG.

This is the Landsat TM reflectance data for Cuprite, Nevada, produced using ENVI's Landsat TM calibration Utility.

  1. Load TM band 3 as a grayscale image in a new image display by choposing the grayscale radio button, clicking on the band name, clicking "New", and then "Load Band" in the Available Bands List.
  2. Start a Z-Profile by selecting Functions->Profiles->Z-Profile in the Main Image Display window and use to examine apparent reflectance spectra.
  3. Start a new plot window from the Basic Tools->Display Controls->Start New Plot Window menu and load the ASCII file CUPTM_EM.TXT. Compare the two spectra to the library spectra in the Spectral Library Viewer. Drag and drop spectra from the Spectral Library Viewer plot window into this new plot window by clicking the right mouse button to the right of the right plot axis to toggle on the spectra names, then clicking and dragging using the left mouse button on the beginning of the spectrum name and releasing the left mouse button when the spectrum name appears in the second plot window.

Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.com

  1. Click the right mouse button in the Main Image Display window to toggle on the Functions menu. Select Functions->Interactive Analysis->Pixel Locator. Use the Pixel Locator to locate and browse around the location of the Kaolinite (248, 351) and the Alunite (260, 330) and examine the spectral variability. Examine spectra near 202, 295 (Buddingtonite) and near 251, 297 (Silica or Opal) and compare to the library spectra. Drag-and-drop spectra for best comparison. Answer Questions pertaining to the TM data below.
  2. Place this plot window to one side of the screen for comparison with data from the other sensors.

View GEOSCAN Data

This dataset is Cuprite GEOSCAN imagery with approximately 60 nm Resolution with 44 nm sampling converted to apparent reflectance using a flat field correction. The GEOSCAN MkII sensor, flown on a light aircraft during the late 1980s was a commercial aircraft system that acquired up to 24 spectral channels selected from 46 available bands. GEOSCAN covered the range from 0.45 to 12.0 mm using grating dispersive optics and three sets of linear array detectors (Lyon and Honey, 1989). A typical data acquisition for geology resulted in 10 bands in the visible/near infrared (VNIR, 0.52 - 0.96 mm), 8 bands in the shortwave infrared (SWIR, 2.04 - 2.35 mm), and thermal infrared (TIR, 8.64 - 11.28 mm) regions (Lyon and Honey, 1990). The GEOSCAN data were acquired in June 1989. The figure below is a plot of the ROI average spectra for the three materials shown in the library spectral plot above. Compare these to library spectra and the Landsat TM spectra and note that the three minerals appear quite different in the GEOSCAN data, even with the relatively widely spaced spectral bands.

Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.comGEOSCAN is high spatial resolution makes it suitable for detailed geologic mapping (Hook et al., 1991). The relatively low number of spectral bands and, low spectral resolution limit mineralogic mapping capabilities to a few groups of minerals in the absence of ground information. Strategic placement of the SWIR bands, however, does provide more mineralogic information than would intuitively be expected based on the spectral resolution limitations.

  1. Select File->Open Image File from the ENVI main menu and open and display the Cuprite GEOSCAN data CUPGS_SB.IMG.
  2. Start a Z-Profile by selecting Functions->Profiles->Z-Profile in the Main Image Display window and use to browse through some of the apparent reflectance spectra.
  3. Load a color composite image of bands 13, 15, and 18 (RGB) to enhance mineralogical differences.
  4. Start a new plot window from the Basic Tools->Display Controls->Start New Plot Window menu and load the ASCII file CUPGS_EM.TXT. Compare the two spectra to the library spectra in the Spectral Library Viewer, and to the spectra from the other sensors. Drag and drop spectra as described above for direct comparison.
  5. Click the right mouse button in the Main Image Display window to toggle on the Functions menu. Select Functions->Interactive Analysis->Pixel Locator. Use the Pixel Locator to locate and browse around the location of the Kaolinite (275, 761) and the Alunite (435, 551) and examine the spectral variability. Examine spectra near 168, 475 (Buddingtonite) and near 371, 592 (Silica or Opal) and compare to the library spectra and spectra from the other sensors. Answer Questions pertaining to the GEOSCAN data below.
  6. Place this plot window to one side of the screen for comparison with data from the other sensors.

View GER63 Data

Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.comThis dataset is Cuprite Geophysical and Environmental Research 63-band scanner data (GER63). It has an advertised spectral resolution of 17.5 nm, but comparison with other sensors and laboratory spectra suggests that 35 nm resolution with 17.5 nm sampling is more likely. Four bad bands have been dropped so that only 59 spectral bands are available. The GER63 data described here were acquired during August 1987. Selected analysis results were previously published in Kruse et al. (1990). The figure below is a plot of the ROI average spectra for the three materials shown in the library spectra above. Note that the GER63 data adequately discriminate the alunite and buddingtonite, but do not fully resolve the kaolinite "doublet" near 2.2 mm shown in the laboratory.

  1. Select File->Open Image File from the ENVI main menu and open and display the Cuprite GER64 data CUPGERSB.IMG.
  2. Start a Z-Profile by selecting Functions->Profiles->Z-Profile in the Main Image Display window and use to browse through some of the apparent reflectance spectra.
  3. Load a color composite image of bands 36, 42, and 50 (RGB) to enhance mineralogical differences.
  4. Start a new plot window from the Basic Tools->Display Controls->Start New Plot Window menu and load the ASCII file CUPGEREM.TXT. Compare the two spectra to the library spectra in the Spectral Library Viewer, and to the spectra from the other sensors. Drag and drop spectra as described above for direct comparison.
  5. Click the right mouse button in the Main Image Display window to toggle on the Functions menu. Select Functions->Interactive Analysis->Pixel Locator. Use the Pixel Locator to locate and browse around the location of the Kaolinite (235, 322) and the Alunite (303, 240) and examine the spectral variability. Examine spectra near 185, 233 (Buddingtonite) and near 289, 253 (Silica or Opal) and compare to the library spectra and spectra from the other sensors. Drag-and-drop spectra for best comparison. Answer Questions pertaining to the GER63 data below.
  6. Place this plot window to one side of the screen for comparison with data from the other sensors.

View AVIRIS Data

Hyperpectral Signatures and Spectral Resolution Using ENVI - facegis.comThese data are Cuprite 1995 Airborne Visible Infrared Imaging Spectrometer (AVIRIS), which have approximately 10 nm spectral resolution and 20 m spatial resolution. The AVIRIS data shown here were acquired during July 1995 as part of an AVIRIS Group Shoot (Kruse and Huntington, 1996). The data were corrected to reflectance using the ATREM method and residual noise was removed using the EFFORT procedure. The figure below is a plot of the ROI average spectra for the three materials shown in the library spectral plot. Compare these to the laboratory spectra above and note the high quality and nearly identical signatures.

  1. Select File->Open File and navigate to the C95AVSUB subdirectory of the ENVIDATA directory on the ENVI TUTORIALS & DATA CD-ROM just as you would in any other application and select the file CUP95EFF.INT from the list and click "OK".
  2. Display a grayscale image and then start a Z-Profile by selecting Functions->Profiles->Z-Profile in the Main Image Display window and use to browse through some of the apparent reflectance spectra.
  3. Load a color composite image of bands 183, 193, and 207 (RGB) to enhance mineralogical differences.
  4. Start a new plot window from the Basic Tools->Display Controls->Start New Plot Window menu and load the ASCII file CUP95EFF.TXT. Compare the two spectra to the library spectra in the Spectral Library Viewer, and to the spectra from the other sensors. Drag and drop spectra as described above for direct comparison.
  5. Click the right mouse button in the Main Image Display window to toggle on the Functions menu. Select Functions->Interactive Analysis->Pixel Locator.Use the Pixel Locator to locate and browse around the location of the Kaolinite (500, 581) and the Alunite (538, 536) and examine the spectral variability. Examine spectra near 447, 484 (Buddingtonite) and 525, 505 (Silica or Opal) and compare to the library spectra and spectra from the other sensors. Drag-and-drop spectra for best comparison. Answer Questions pertaining to the 1995 AVIRIS data below.

Evaluate Sensor Capabilities Vs ID Requirements

These four sensors and the library spectra represent a broad range of spectral resolutions.

  1. Using the Library Spectra as the ground truth, evaluate how well each of the sensors is able to represent the ground truth information. Consider what it means to discriminate between materials versus identification of materials.

Draw Conclusions

  1. Using the selected library spectra provided, what is the minimum spacing of absorption features in the 2.0 - 2.5 mm range?
  2. The TM data obviously dramatically undersample the 2.0 - 2.5 mm range, as only TM band 7 is available. What evidence do you see for absorption features in this range? What differences are apparent in the TM spectra of minerals with absorption features in this range?
  3. The GEOSCAN data also undersample the 2.0 - 2.5 mm range, however, the bands are strategically placed. What differences are there between the GEOSCAN spectra for the different minerals? Could some of the bands have been placed differently to provide better mapping of specific minerals?
  4. The GER63 data provide improved spectral resolution over the GEOSCAN data and individual features can be observed. The advertised spectral resolution of the GER63 between 2.0 - 2.5 mm is 17.5 nm. Examine the GER-63 kaolinite spectrum and defend or refute this resolution specification. Do the more closely spaced spectral bands of the GER63 sensor provide a significant advantage over the GEOSCAN data in mapping and identifying these reference minerals?
  5. The AVIRIS data provide the best spectral resolution of the sensors examined here. How do the AVIRIS and laboratory spectra compare? What are the major similarities and differences? What factors affect the comparison of the two data types?
  6. Examine all of the images and spectra. What role does spatial resolution play in the comparison?
  7. Based on the Library spectra, provide sensor spectral and spatial resolution design specifications as well as recommendations on placement of spectral bands for mineral mapping. Examine the tradeoffs between continuous high-spectral resolution bands and strategically placed, lower resolution bands

Selected References of Interest

Abrams, M. J., Ashley, R. P., Rowan, L. C., Goetz, A. F. H., and Kahle, A. B., 1978, Mapping of hydrothermal alteration in the Cuprite Mining District, Nevada using aircraft scanner images for the spectral region 0.46 - 2.36 mm: Geology, v. 5., p. 173 - 718.

Abrams, M., and Hook, S. J., 1995, Simulated ASTER data for Geologic Studies: IEEE Transactions on Geoscience and Remote Sensing, v. 33, no. 3, p. 692 - 699.

Chrien, T. G., Green, R. O., and Eastwood, M. L., 1990, Accuracy of the spectral and radiometric laboratory calibration of the Airborne Visible/Infrared Imaging Spectrometer: in Proceedings The International Society for Optical Engineering (SPIE), v. 1298, p. 37-49.

Clark, R. N., King, T. V. V., Klejwa, M., and Swayze, G. A., 1990, High spectral resolution spectroscopy of minerals: Journal of Geophysical Research, v. 95, no., B8, p. 12653 - 12680.

Clark, R. N., Swayze, G. A., Gallagher, A., King, T. V. V., and Calvin, W. M., 1993, The U. S. Geological Survey Digital Spectral Library: Version 1: 0.2 to 3.0 mm: U. S. Geological Survey, Open File Report 93-592, 1340 p.

CSES, 1992, Atmosphere REMoval Program (ATREM) User's Guide, Version 1.1, Center for the Study of Earth from Space, Boulder, Colorado, 24 p.

Goetz, A. F. H., and Kindel, B., 1996, Understanding unmixed AVIRIS images in Cuprite, NV using coincident HYDICE data: in Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, March 4-8, 1996, v. 1 (Preliminary).

Goetz, A. F. H., and Rowan, L. C., 1981, Geologic Remote Sensing: Science, v. 211, p. 781 - 791.

Goetz, A. F. H., Rock, B. N., and Rowan, L. C., 1983, Remote Sensing for Exploration: An Overview: Economic Geology, v. 78, no. 4, p. 573 - 590.

Goetz, A. F. H., Vane, G., Solomon, J. E., and Rock , B. N., 1985, Imaging spectrometry for earth remote sensing: Science, v. 228, p. 1147 - 1153.

Green, R. O., Conel, J. E., Margolis, J., Chovit, C., and Faust, J., 1996, In-flight calibration and validation of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS): in Summaries of the Sixth Annual JPL Airborne Geoscience Workshop, 4-8 March 1996, Jet Propulsion Laboratory, Pasadena, CA, v. 1, (Preliminary).

Hook, S. J., Elvidge, C. D., Rast, M., and Watanabe, H., 1991, An evaluation of short-wave-infrared (SWIR) data from the AVIRIS and GEOSCAN instruments for mineralogic mapping at Cuprite, Nevada: Geophysics, v. 56, no. 9, p. 1432 - 1440.

Kruse, F. A., 1988, Use of Airborne Imaging Spectrometer data to map minerals associated with hydrothermally altered rocks in the northern Grapevine Mountains, Nevada and California: Remote Sensing of Environment, V. 24, No. 1, p. 31-51.

Kruse, F. A., and Huntington, J. H., 1996, The 1995 Geology AVIRIS Group Shoot: in Summaries of the Sixth Annual JPL Airborne Earth Science Workshop, March 4 - 8, 1996 Volume 1, AVIRIS Workshop, (Preliminary).

Kruse, F. A., Kierein-Young, K. S., and Boardman, J. W., 1990, Mineral mapping at Cuprite, Nevada with a 63 channel imaging spectrometer: Photogrammetric Engineering and Remote Sensing, v. 56, no. 1, p. 83-92.

Lyon, R. J. P., and Honey, F. R., 1989, spectral signature extraction from airborne imagery using the Geoscan MkII advanced airborne scanner in the Leonora, Western Australia Gold District: in IGARSS-89/12th Canadian Symposium on Remote Sensing, v. 5, p. 2925 - 2930.

Lyon, R.J. P., and Honey, F. R., 1990, Thermal Infrared imagery from the Geoscan Mark II scanner of the Ludwig Skarn, Yerington, NV: in Proceedings of the Second Thermal Infrared Multispectral Scanner (TIMS) Workshop.

Paylor, E. D., Abrams, M. J., Conel, J. E., Kahle, A. B., and Lang, H. R., 1985, Performance evaluation and geologic utility of Landsat-4 Thematic Mapper Data: JPL Publication 85-66, Jet Propulsion Laboratory, Pasadena, CA, 68 p.

Pease, C. B., 1990, Satellite imaging instruments: Principles, Technologies, and Operational Systems: Ellis Horwid, N.Y., 336 p.

Porter, W. M., and Enmark, H. E., 1987, System overview of the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), in Proceedings, Society of Photo-Optical Instrumentation Engineers (SPIE), v. 834, p. 22-31.

Swayze, Gregg, 1997, The hydrothermal and structural history of the Cuprite Mining District, Southwestern Nevada: an integrated geological and geophysical approach: Unpublished Ph. D. Dissertation, University of Colorado, Boulder.

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